from sklearn.preprocessing import StandardScaler
import numpy as np




raw_data_X = [[3.393533211, 2.331273381],
              [3.110073483, 1.781539638],
              [1.343808831, 3.368360954],
              [3.582294042, 4.679179110],
              [2.280362439, 2.866990263],
              [7.423436942, 4.696522875],
              [5.745051997, 3.533989803],
              [9.172168622, 2.511101045],
              [7.792783481, 3.424088941],
              [7.939820817, 0.791637231],
              [7.792783481, 2.424088941],
              [7.939820817, 1.791637231],
              [7.792783481, 2.8024088941],
              [7.939820817, 4.791637231]
              ]
raw_data_y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,1,0]

X_train = np.array(raw_data_X)
y_train = np.array(raw_data_y)

standscalar=StandardScaler()
standscalar.fit(X_train)
#归一化数据
X_train=standscalar.transform(X_train)
print(X_train[:,:])


